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disaggregated inference thoughts: what are you seeing?
#1
Posting this in case it saves someone else a little time.

I'm helping a small team set up a local workflow. We don't need a giant stack, but we do care about tokens/sec.

Would rather learn from your scars than collect my own all at once.

For context, I'm not chasing leaderboard numbers. I mostly want the system to feel consistent day to day.

The part that surprised me was how much the boring details mattered more than the model choice.
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#2
Not sure I agree there. I get why that seems right, but on Threadripper box the bottleneck felt elsewhere.
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#3
Ran a quick check on my box. My result was mixed: first run looked bad, second run looked normal. At this point the real bottleneck is my patience.
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#4
I tested this tonight. I couldn't reproduce the slowdown until I pushed context much higher. The main thing that moved was latency, not quality. Turns out 'just one more test' was a lie.
mostly local
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#5
Quote:I tested this tonight. I couldn't reproduce the slowdown until I pushed context much higher. The main thing that moved was latency, not quality. Turns

I'd approach it in a boring order. Start by verifying the driver/runtime combo, then test with a smaller prompt, then look at HBM constraints. A lot of these threads end up being one bad config plus a pile of conclusions drawn from it.
2x3090 • llama.cpp • too much VRAM is still not enough
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#6
That matches what I was seeing. The pattern around prefix caching has been pretty similar for me.
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#7
Thanks, that helps. That matches what I saw on 7900 XTX. I got better results once I stopped chasing one metric and just looked at whether the workflow felt usable. At this point the real bottleneck is my patience.
small models can still be good
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#8
Interesting, I hadn't tried that. I don't think your conclusion is far off. I got better results once I stopped chasing one metric and just looked at whether the workflow felt usable.
still benchmarking everything
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#9
I'd approach it in a boring order. Before rebuilding anything, I'd test one clean baseline with Llama and compare from there. A lot of these threads end up being one bad config plus a pile of conclusions drawn from it.
mostly local
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#10
Thanks, that helps. I ended up in about the same place using Ollama. The biggest change for me was simplifying the stack instead of adding one more optimization layer.
small models can still be good
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